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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sources NASA NTRS DOE OSTI

DOE OSTI2024

Liquid-like solid-state diffusion of lithium ions in super-halide-rich argyrodite

The development of solid electrolytes with high ionic conductivity is essential for advancing safer, high-energy-density solid-state batteries, where lithium site distribution in the sublattice strongly affects ion transport. Here, we report a super-halide-rich argyrodite, Li 5.3 PS 4.3 Cl 1.7 , with remarkable room-temperature ionic conductivity (11.4 ± 0.7 mS cm -1 ) due to population of two additional interstitial lithium sites induced by vacancy redistribution. Prominent lithium density between lithium sites and elevated atomic displacement parameters indicate liquid-like diffusive behavior resembling sublattice melting. Combining electrochemical impedance spectroscopy, pulsed-field gradient NMR, and T 1 relaxation methods, we demonstrate that the augmented conductivity partly arises from a low energy barrier (0.08 eV) at the local scale, attributed to a three-site lithium distribution that drives correlated lithium dynamics. This work advances our understanding of the structure-dynamics interplay in super-halide-rich argyrodites, and highlighting their potential as solid-state battery electrolytes in cells with a coated single-crystal NMC82 cathode that achieve 170 mAh/g capacity at a 0.2 C rate .

25 ENERGY STORAGE↗

DOE OSTI2023

Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States

Abstract Amplification in extreme precipitation intensity and frequency can cause severe flooding and impose significant social and economic consequences. Variations in extreme precipitation intensity, frequencies, and return periods can be attributed to many physical variables across spatial and temporal scales. Here we employ ensemble machine learning (ML) methods, namely random forest (RF), eXtreme Gradient Boosting (XGB), and artificial neural networks (ANN), to explore key contributing variables to monthly extreme precipitation intensity and frequency in six regions over the United States. We further establish emulators for return periods. Results show that the ML models for intensity perform better in regions with obvious seasonality (i.e., Northern Great Plains, Southern Great Plains, and West Coast) than the other three regions (Northeast, Southwest, and Rocky Mountains), while for frequency the models perform well for most regions. The Shapley additive explanation is used to help explain the relationships between extreme precipitation characteristics and identify top variables for RF and XGB. We find that latent heat flux, relative humidity, soil moisture, and large‐scale subsidence are key common variables across the regions for both monthly intensity and frequency, and their compound effects are non‐negligible. The developed ML models capture the probability and return period of extreme precipitation well for all regions and may be used for decision making (e.g., infrastructure planning and design).

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Rethinking materials simulations: Blending direct numerical simulations with neural operators

Abstract Materials simulations based on direct numerical solvers are accurate but computationally expensive for predicting materials evolution across length- and time-scales, due to the complexity of the underlying evolution equations, the nature of multiscale spatiotemporal interactions, and the need to reach long-time integration. We develop a method that blends direct numerical solvers with neural operators to accelerate such simulations. This methodology is based on the integration of a community numerical solver with a U-Net neural operator, enhanced by a temporal-conditioning mechanism to enable accurate extrapolation and efficient time-to-solution predictions of the dynamics. We demonstrate the effectiveness of this hybrid framework on simulations of microstructure evolution via the phase-field method. Such simulations exhibit high spatial gradients and the co-evolution of different material phases with simultaneous slow and fast materials dynamics. We establish accurate extrapolation of the coupled solver with large speed-up compared to DNS depending on the hybrid strategy utilized. This methodology is generalizable to a broad range of materials simulations, from solid mechanics to fluid dynamics, geophysics, climate, and more.

36 MATERIALS SCIENCE↗

DOE OSTI2023

Perfect Crystals: microgravity capillary counterdiffusion crystallization of human manganese superoxide dismutase for neutron crystallography

The NASA mission Perfect Crystals used the microgravity environment on the International Space Station (ISS) to grow crystals of human manganese superoxide dismutase (MnSOD)—an oxidoreductase critical for mitochondrial vitality and human health. The mission’s overarching aim is to perform neutron protein crystallography (NPC) on MnSOD to directly visualize proton positions and derive a chemical understanding of the concerted proton electron transfers performed by the enzyme. Large crystals that are perfect enough to diffract neutrons to sufficient resolution are essential for NPC. This combination, large and perfect, is hard to achieve on Earth due to gravity-induced convective mixing. Capillary counterdiffusion methods were developed that provided a gradient of conditions for crystal growth along with a built-in time delay that prevented premature crystallization before stowage on the ISS. Here, we report a highly successful and versatile crystallization system to grow a plethora of crystals for high-resolution NPC.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2023

CFD-based design optimization of ducted hydrokinetic turbines

Abstract Hydrokinetic turbines extract kinetic energy from moving water to generate renewable electricity, thus contributing to sustainable energy production and reducing reliance on fossil fuels. It has been hypothesized that a duct can accelerate and condition the fluid flow passing the turbine blades, improving the overall energy extraction efficiency. However, no substantial evidence has been provided so far for hydrokinetic turbines. To investigate this problem, we perform a CFD-based optimization study with a blade-resolved Reynolds-averaged Navier–Stokes (RANS) solver to explore the design of a ducted hydrokinetic turbine that maximizes the efficiency of energy extraction. A gradient-based optimization approach is utilized to effectively deal with the high-dimensional design space of the blade and duct geometry, with gradients being calculated through the adjoint method. The final design is re-evaluated through higher-fidelity unsteady RANS (URANS) simulations. Our optimized ducted turbine achieves an efficiency of about 54% over a range of operating conditions, higher than the typical 46% efficiency of unducted turbines.

13 HYDRO ENERGY↗

DOE OSTI2020

Detection of topological materials with machine learning

Databases compiled using ab initio and symmetry-based calculations now contain tens of thousands of topological insulators and topological semimetals. This makes the application of modern machine learning methods to topological materials possible. Using gradient boosted trees, we show how to construct a machine learning model which can predict the topology of a given existent material with an accuracy of 90%. Such predictions are orders of magnitude faster than actual ab initio calculations. In this work, we use machine learning models to probe how different material properties affect topological features. Notably, we observe that topology is mostly determined by the “coarse-grained” chemical composition and crystal symmetry and depends little on the particular positions of atoms in the crystal lattice. We identify the sources of our model's errors and we discuss approaches to overcome them.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2021

Prospects for the expansion of standing wave ambient pressure photoemission spectroscopy to reactions at elevated temperatures

Standing wave ambient pressure photoemission spectroscopy (SWAPPS) is a promising method to investigate chemical and potential gradients across solid-vapor and solid-liquid interfaces under close-to-realistic environmental conditions, far away from high vacuum. Until now, these investigations have been performed only near room temperature, but for a wide range of interfacial processes, chief among them being heterogeneous catalysis, measurements at elevated temperatures are required. One concern in these investigations is the temperature stability of the multilayer mirrors, which generate the standing wave field. At elevated temperatures, degradation of the multilayer mirror due to, for example, interdiffusion between the adjacent layers, decreases the modulation of the standing wave field, thus rendering SWAPPS experiments much harder to perform. Here, we show that multilayer mirrors consisting of alternate B4C and W layers are stable at temperatures exceeding 600 °C and are, thus, promising candidates for future studies of surface and subsurface species in heterogeneous catalytic reactions using SWAPPS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2024

Stochastic minibatch approach to the ptychographic iterative engine

The ptychographic iterative engine (PIE) is a widely used algorithm that enables phase retrieval at nanometer-scale resolution over a wide range of imaging experiment configurations. By analyzing diffraction intensities from multiple scanning locations where a probing wavefield interacts with a sample, the algorithm solves a difficult optimization problem with constraints derived from the experimental geometry as well as sample properties. The effectiveness at which this optimization problem is solved is highly dependent on the ordering in which we use the measured diffraction intensities in the algorithm, and random ordering is widely used due to the limited ability to escape from stagnation in poor-quality local solutions. In this study, we introduce an extension to the PIE algorithm that uses ideas popularized in recent machine learning training methods, in this case minibatch stochastic gradient descent. Our results demonstrate that these new techniques significantly improve the convergence properties of the PIE numerical optimization problem.

47 OTHER INSTRUMENTATION↗

DOE OSTI2022

Scalable Second Order Optimization for Machine Learning

Many machine learning (ML) training tasks are essentially optimization processes that would at first glance appear eminently parallelizable and scalable. However, effective acceleration of these tasks with scalable parallel hardware has proven to be elusive. While standard methods for machine learning, e.g., stochastic gradient descent (SGD) for DNNs, tend to be resource efficient, they appear to be fundamentally sequential in nature.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2025

Beyond Magic Barrels: Digital manufacturing for crystallization, process development and optimization of explosive materials: Part II Resveratrol Exemplar

This SAND report summarizes work supported by an Engineering Sciences Research Foundation (ESRF) Lab Directed Research and Development (LDRD) project entitled “Beyond Magic Barrels: Digital manufacturing for crystallization, process development and optimization of explosive materials.” This SAND report is written in two parts with Part 1 discusses recrystallization of our explosive exemplar and Part 2 summarizing our work with recrystallization of resveratrol. We have studied resveratrol recrystallization with a multiscale approach combining experiments, modeling and simulation. At the single crystal scale, microscopy experiments illuminate crystal time-dependent growth rates using advanced image analysis. Bench scale experiments were carried out to look at growth of multiple particles in a small reactor creating thousands of particles and analyzing the results with microscopy and μCT. For the modeling we combine kinetic Monte Carlo (kMC) models with subscale information from density functional theory (DFT) or molecular dynamics. This work is discussed in Part 1 and can also be found in a paper from the project discussing a coarse-grained kMC model specifically developed for resveratrol. For well-mixed systems, we have population balance equations (PBE) linked with species mass conservation forming a set of ordinary differential equations that can be solved quickly. For more complicated geometries, such as the vat crystallization used throughout the complex, a coupled computational fluid dynamic (CFD)/PBE method was developed to account for gradients in temperature and concentration and differences in crystallization rates throughout the domain. These simulations are more complex and require high performance computing. We present results for two cases: 5% seed fast cool with parameters fit to the well-mixed case and 5% seed slow cool using the same parameters. We show reasonable agreement with experiments though are particles are significantly larger than the experiments.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Prairie Strips and Lower Land Use Intensity Increase Biodiversity and Ecosystem Services

Agricultural landscapes can be managed to protect biodiversity and maintain ecosystem services. One approach to achieve this is to restore native perennial vegetation within croplands. Where rowcrops have displaced prairie, as in the US Midwest, restoration of native perennial vegetation can align with crops in so called “prairie strips.” We tested the effect of prairie strips in addition to other management practices on a variety of taxa and on a suite of ecosystem services. To do so, we worked within a 33-year-old experiment that included treatments that varied methods of agricultural management across a gradient of land use intensity. In the two lowest intensity crop management treatments, we introduced prairie strips that occupied 5% of crop area. We addressed three questions: (1) What are the effects of newly established prairie strips on the spillover of biodiversity and ecosystem services into cropland? (2) How does time since prairie strip establishment affect biodiversity and ecosystem services? (3) What are the tradeoffs and synergies among biodiversity conservation, non-provisioning ecosystem services, and provisioning ecosystem services (crop yield) across a land use intensity gradient (which includes prairie strips)? Within prairie strip treatments, where sampling effort occurred within and at increasing distance from strips, dung beetle abundance, spider abundance and richness, active carbon, decomposition, and pollination decreased with distance from prairie strips, and this effect increased between the first and second year. Across the entire land use intensity gradient, treatments with prairie strips and reduced chemical inputs had higher butterfly abundance, spider abundance, and pollination services. In addition, soil organic carbon, butterfly richness, and spider richness increased with a decrease in land use intensity. Crop yield in one treatment with prairie strips was equal to that of the highest intensity management, even while including the area taken out of production. We found no effects of strips on ant biodiversity and greenhouse gas emissions (N 2 O and CH 4 ). Our results show that, even in early establishment, prairie strips and lower land use intensity can contribute to the conservation of biodiversity and ecosystem services without a disproportionate loss of crop yield.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

kemmerl4/LTER_prairie_strip_2019-2020: Data for: Prairie strips and lower land use intensity increase biodiversity and ecosystem services

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS1962

MEASURING THE DENSITY DISTRIBUTION IN A THREE-DIMENSIONAL OBJECT USING THE SCHLIEREN METHOD

Approximation method for measuring temperature and pressure gradients and density distribution in unsymmetrical objects using schlieren photographs

PRESSURE GRADIENT↗

NASA NTRS1974

Heat flow and geothermal potential of the East Mesa KGRA, Imperial Valley, California

The East Mesa KGRA (Known Geothermal Resource Area) is located in the southeast part of the Imperial Valley, California, and is roughly 150 kilometers square in areal extent. A new heat flow technique which utilizes temperature gradient measurements across best clays is presented and shown to be as accurate as conventional methods for the present study area. Utilizing the best clay gradient technique, over 70 heat flow determinations have been completed within and around the East Mesa KGRA. Background heat flow values range from 1.4 to 2.4 hfu (1 hfu = .000001 cal. per square centimeter-second) and are typical of those throughout the Basin and Range province. Heat flow values for the northwest lobe of the KGRA (Mesa anomaly) are as high as 7.9 hfu, with the highest values located near gravity and seismic noise maxima and electrical resistivity minima. An excellent correlation exists between heat flow contours and faults defined by remote sensing and microearthquake monitoring.

Swanberg, C. A.↗

NASA NTRS1975

Numerical computation of viscous flows on the lee side of blunt shapes flying at supersonic speeds

A numerical method for solving the parabolic approximation to the steady-state compressible Navier-Stokes equations is examined. The approximation neglects only the streamwise gradients of shear stress. An implicit finite difference method is used which advances the solution downstream from an initial data surface and determines the complete viscous-inviscid flow between the body and bow shock wave. It is necessary that the inviscid portion of the flow field be supersonic. Crossflow separation is determined as part of the solution. The method is applied to a 15 deg sphere-cone at 15 deg angle of attack, and the results are compared with an inviscid method-of-characteristics calculation.

Rakich, J. V.↗

NASA NTRS1983

Mixed solar wind originating from coronal regions of different temperatures

Ionization states of elements in the solar wind are often used to determine thermal gradients in the lower corona. This method is based on the assumption, that in the beginning, solar wind material has a homogeneous temperature determining the original charge state of elements. Features in M/Q-spectra which might appear if the above assumption is violated are investigated and compared with observational evidence.

Bochsler, P.↗

NASA NTRS1984

Simulated flight through JAWS wind shear - In-depth analysis results

The Joint Airport Weather Studies (JAWS) field experiment was carried out in 1982 near Denver. An analysis is presented of aircraft performance in the three-dimensional wind fields. The fourth dimension, time, is not considered. The analysis seeks to prepare computer models of microburst wind shear from the JAWS data sets for input to flight simulators and for research and development of aircraft control systems and operational procedures. A description is given of the data set and the method of interpolating velocities and velocity gradients for input to the six-degrees-of-freedom equations governing the motion of the aircraft. The results of the aircraft performance analysis are then presented, and the interpretation classifies the regions of shear as severe, moderate, or weak. Paths through the severe microburst of August 5, 1982, are then recommended for training and operational applications. Selected subregions of the flow field defined in terms of planar sections through the wind field are presented for application to simulators with limited computer storage capacity, that is, for computers incapable of storing the entire array of variables needed if the complete wind field is programmed.

Frost, W.↗

NASA NTRS1988

Transient natural convection heat and mass transfer in a rectangular enclosure - A numerical analysis

A numerical analysis of transient heat and solute transport across a rectangular cavity with combined horizontal temperature and concentration gradients is performed by a numerical method based on the SIMPLE. Numerical results show that the average Nusselt and Sherwood numbers both decrease markedly when the solutal and thermal buoyancy forces act in the opposite directions. When the solutal and thermal buoyancy forces act in the same directions, however, the average Sherwood number increases significantly and yet the average Nusselt number decreases slightly.

Han, Samuel S.↗